Permutation Importance [1]}, [2]}, [3]}, [4]}, is used in answering how a certain feature influence the overall prediction, as it evaluates the changes of model prediction's accuracy by permuting the feature's values. If let \(s\)  represent the model accuracy with the full dataset \(D\) , then the permutation feature importance of \(i^{th}\)  feature is:
\(Per_{Imp}(i) = s - \frac{1}{K} \sum _{j=1}^K s_{i, j}, \) 
